Tanner Collin
89b407c564
Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>
84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
import torch
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import torch.nn.functional as F
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from torchvision import transforms
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from PIL import Image
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import os
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import shutil
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from model import (CropLowerRightTriangle, GarageDoorCNN, TRIANGLE_CROP_WIDTH,
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TRIANGLE_CROP_HEIGHT, RESIZE_DIM)
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def sort_images():
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# --- Configuration ---
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MODEL_PATH = 'garage_door_cnn.pth'
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SOURCE_DIR = 'data/hourly_photos/'
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DEST_DIR = 'data/sorted/open/'
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CONFIDENCE_THRESHOLD = 0.95 # Only copy if confidence is over this value
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# The classes are sorted alphabetically by ImageFolder: ['closed', 'open']
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CLASS_NAMES = ['closed', 'open']
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TARGET_CLASS = 'open'
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TARGET_CLASS_IDX = CLASS_NAMES.index(TARGET_CLASS)
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# --- Setup ---
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# Create destination directory if it doesn't exist
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os.makedirs(DEST_DIR, exist_ok=True)
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# Set up device
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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# Load model
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model = GarageDoorCNN(resize_dim=RESIZE_DIM)
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model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
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model.to(device)
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model.eval()
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# Define image transforms
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data_transform = transforms.Compose([
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CropLowerRightTriangle(triangle_width=TRIANGLE_CROP_WIDTH, triangle_height=TRIANGLE_CROP_HEIGHT),
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transforms.Resize((RESIZE_DIM, RESIZE_DIM)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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])
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# --- Process Images ---
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print(f"Scanning images in {SOURCE_DIR}...")
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with torch.no_grad():
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for filename in os.listdir(SOURCE_DIR):
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file_path = os.path.join(SOURCE_DIR, filename)
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if os.path.isfile(file_path):
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try:
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image = Image.open(file_path).convert('RGB')
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# Apply transformations
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input_tensor = data_transform(image)
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input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
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input_batch = input_batch.to(device)
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# Get model output
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output = model(input_batch)
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# Get probabilities and prediction
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probabilities = F.softmax(output, dim=1)
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confidence, pred_idx = torch.max(probabilities, 1)
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if pred_idx.item() == TARGET_CLASS_IDX and confidence.item() > CONFIDENCE_THRESHOLD:
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print(f"Found 'open' image: {file_path} with confidence: {confidence.item():.4f}")
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# Copy file
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shutil.copy(file_path, os.path.join(DEST_DIR, filename))
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except Exception as e:
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print(f"Could not process file {file_path}: {e}")
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print("Sorting complete.")
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if __name__ == '__main__':
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if not os.path.exists('garage_door_cnn.pth'):
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print("Error: Model file 'garage_door_cnn.pth' not found. Please run train.py first.")
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elif not os.path.isdir('data/hourly_photos'):
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print("Error: Source directory 'data/hourly_photos' not found.")
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else:
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sort_images()
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